Inductive Inference and Beyond: From Bayesian Rationality to
Logical Reliability

Prasanta S. Bandyopadhyay
Montana State University

Traditional epistemology fails to provide a satisfactory account of inductive inference because the notion of justification raises a host of problems. One well-known approach to induction is Bayesianism that exploits the concept of rationality. Bayesianism is, however, alleged to rest on dubious assumptions. As an alternative, logical reliabilism (or Android epistemology) defended by Glymour, Kelly, and Juhl provides an account of induction that draws its inspiration from computability theory and the formal theory of learning. Logical reliabilists contend that there are similarities between induction and computability theory. In addition, formal learning theory provides necessary tools for them to develop algorithms for key episodes in the history of science.

I develop and defend Bayesianism. I contend that logical reliabilism takes for granted certain questionable assumptions, too. The important question is which assumptions are more reasonable. I also argue that my approach provides a more plausible and more intuitive reconstruction of some episodes of science than theirs.